code: def autoparallelize(
      model: nn.Module,
      meta_args: Dict[str, torch.Tensor] = None,
      data_loader: torch.utils.data.DataLoader = None,
      data_process_func: callable = None,
      alpha_beta_dict: Dict[Tuple[int], Tuple[float]] = None,
      logical_mesh_shape: Tuple[int] = None,
      logical_mesh_id: torch.Tensor = None,
      solver_preference: str = "standard",
      dataloader_option: str = "replicated",
      shard_option: str = "standard",
      save_solver_solution: bool = False,
      load_solver_solution: bool = False,
      solver_solution_path: str = None,
      return_solution: bool = False,
      memory_budget: float = -1.0,
  ):
      """
      This method is used to initialize the device mesh, extract the meta_args, and
      use them to create a sharded model.
  
      Args:
          model: the model to be sharded.
          meta_args(optional): the meta_args is used to specify the input shapes of the model.
              If the meta_args is None, the meta_args will be extracted from the data_loader.
          data_loader(optional): the data_loader to be used in normal training loop.
          data_process_func(optional): the data_process_func is used to process the data from the data_loader.
          alpha_beta_dict(optional): the alpha_beta_dict contains the alpha and beta values
              for each devices. if the alpha_beta_dict is None, the alpha_beta_dict will be
              generated by profile_alpha_beta function.
          logical_mesh_shape(optional): the logical_mesh_shape is used to specify the logical
              mesh shape. If the logical_mesh_shape is None, the logical_mesh_shape will be
              generated by search_best_logical_mesh_shape function.
          logical_mesh_id(optional): the logical_mesh_id is used to specify the logical mesh id.
          solver_preference(optional): the solver_preference is used to specify which parallelism algorithm
              has higher priority. The valid solver_preference could be 'standard', 'tp', or 'dp'.
          dataloader_option(optional): the dataloader_option is used to specify which kind of data_loader will
              be used. The valid dataloader_option could be 'replicated' or 'distributed'.
          shard_option(optional): the shard_option is used to specify how many axes will be used to shard the
              model. The valid shard_option could be 'standard', 'shard', 'shard_last_axis', or 'full_shard'.
          save_solver_solution(optional): if the save_solver_solution is True, the solution will be saved
              to the solution_path.
          load_solver_solution(optional): if the load_solver_solution is True, the solution will be loaded
              from the solution_path.
          solver_solution_path(optional): the path to save or load the solution.
          return_solution(optional): if the return_solution is True, the solution will be returned.
          memory_budget(optional): the max cuda memory could be used. If the memory budget is -1.0,
              the memory budget will be infinity.
      """
      device_mesh = initialize_device_mesh(
          alpha_beta_dict=alpha_beta_dict,
          logical_mesh_shape=logical_mesh_shape,
          logical_mesh_id=logical_mesh_id,
      )
      if meta_args is None:
          meta_args = extract_meta_args_from_dataloader(
              data_loader, data_process_func
          )
  
      rst_to_unpack = initialize_model(
          model,
          meta_args,
          device_mesh,
          solver_preference=solver_preference,
          dataloader_option=dataloader_option,
          shard_option=shard_option,
          save_solver_solution=save_solver_solution,
          load_solver_solution=load_solver_solution,
          solution_path=solver_solution_path,
          return_solution=return_solution,
          memory_budget=memory_budget,
      )
  
      if return_solution:
          model, solution = rst_to_unpack
          return model, solution
      else:
          model = rst_to_unpack
          return model
  
imports: from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  from torch.fx.graph import Graph
  from colossalai._analyzer.fx.codegen import ActivationCheckpointCodeGen
  from colossalai._analyzer.fx.graph_module import ColoGraphModule
  from colossalai._analyzer.fx.passes import shape_prop_pass
  from colossalai._analyzer.fx.tracer.tracer import ColoTracer
  from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
  from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
  from colossalai.auto_parallel.tensor_shard.options import DataloaderOption, ShardOption, SolverOptions, SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommAction
  from colossalai.auto_parallel.tensor_shard.solver import CostGraph, Solver, StrategiesConstructor
  from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.tensor.sharding_spec import ShardingSpec
label_desc: Use image node_id 8 for calling a global function with example usage: autoparallelize(model, meta_args, data_loader, data_process_func, alpha_beta_dict, logical_mesh_shape, logical_mesh_id, solver_preference, dataloader_option, shard_option, save_solver_solution, load_solver_solution, solver_solution_path, return_solution, memory_budget) and returns: model,  solution, model